Robust Processing of Natural Language

Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by ``normal'' means. This schema implies, that error detection always comes prior to error handling, a behaviour which hardly can compete with its human model, where many erroneous situations are treated without even noticing them. The paper analyses the necessary preconditions for achieving a higher degree of robustness in natural language processing and suggests a quite different approach based on a procedure for structural disambiguation. It not only offers the possibility to cope with robustness issues in a more natural way but eventually might be suited to accommodate quite different aspects of robust behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro, pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture Notes in Computer Science, Springer 199

The kindergarten-path effect revisited: children’s use of context in processing structural ambiguities

Research with adults has shown that ambiguous spoken sentences are resolved efficiently, exploiting multiple cues—including referential context—to select the intended meaning. Paradoxically, children appear to be insensitive to referential cues when resolving ambiguous sentences, relying instead on statistical properties intrinsic to the language such as verb biases. The possibility that children’s insensitivity to referential context may be an artifact of the experimental design used in previous work was explored with 60 4- to 11-year-olds. An act-out task was designed to discourage children from making incorrect pragmatic inferences and to prevent premature and ballistic responses by enforcing delayed actions. Performance on this task was compared directly with the standard act-out task used in previous studies. The results suggest that young children (5 years) do not use contextual information, even under conditions designed to maximize their use of such cues, but that adult-like processing is evident by approximately 8 years of age. These results support and extend previous findings by Trueswell and colleagues (Cognition (1999), Vol. 73, pp. 89–134) and are consistent with a constraint-based learning account of children’s linguistic development.</p

The processing of ambiguous sentences by first and second language learners of English

This study compares the way English-speaking children and adult second language learners of English resolve relative clause attachment ambiguities in sentences such as The dean liked the secretary of the professor who was reading a letter. Two groups of advanced L2 learners of English with Greek or German as their L1 participated in a set of off-line and on-line tasks. While the participants &apos; disambiguation preferences were influenced by lexical-semantic properties of the preposition linking the two potential antecedent NPs (of vs. with), there was no evidence that they were applying any structure-based ambiguity resolution strategies of the type that have been claimed to influence sentence processing in monolingual adults. These findings differ markedly from those obtained from 6 to 7 yearold monolingual English children in a parallel auditory study (Felser, Marinis, &amp; Clahsen, submitted) in that the children&apos;s attachment preferences were not affected by the type of preposition at all. We argue that whereas children primarily rely on structure-based parsing principles during processing, adult L2 learners are guided mainly by non-structural informatio

Verb Bias in Mandarin Relative Clause Processing

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Real bad grammar: realistic grammatical description with grammaticality

Sampson (this issue) argues for a concept of “realistic grammatical description” in which the distinction between grammatical and ungrammatical sentences is irrelevant. In this article I also argue for a concept of “realistic grammatical description” but one in which a binary distinction between grammatical and ungrammatical sentences is maintained. In distinguishing between the grammatical and ungrammatical, this kind of grammar differs from that proposed by Sampson, but it does share the important property that invented sentences have no role to play, either as positive or negative evidence

Sentence disambiguation by a shift-reduce parsing technique

Native speakers of English show definite and consistent preferences for certain readings of syntactically ambiguous sentences. A user of a natural-language-processing system would naturally expect it to reflect the same preferences. Thus, such systems must model in some way the linguistic performance as well as the linguistic competence of the native speaker. We have developed a parsing algorithm---a variant of the LALR(1) shift-reduce algorithm---that models the preference behavior of native speakers for a range of syntactic preference phenomena reported in the psycholinguistic literature, including the recent data on lexical preferences. The algorithm yields the preferred parse deterministically, without building multiple parse trees and choosing among them. As a side effect, it displays appropriate behavior in processing the much discussed garden-path sentences. The parsing algorithm has been implemented and has confirmed the feasibility of our approach to the modeling of these phenomena.Engineering and Applied Science

Interference in native and non-native sentence processing

The primary aim of my target article was to demonstrate how careful consideration of the working memory operations that underlie successful language comprehension is crucial to our understanding of the similarities and differences between native (L1) and non-native (L2) sentence processing. My central claims were that highly proficient L2 speakers construct similarly specified syntactic parses as L1 speakers, and that differences between L1 and L2 processing can be characterised in terms of L2 speakers being more prone to interference during memory retrieval operations. In explaining L1/L2 differences in this way, I argued a primary source of differences between L1 and L2 processing lies in how different populations of speakers weight cues that guide memory retrieval